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Factoring Risks into Financial Forecasts and Planning

If you asked finance chiefs to list the major uncertainties they’ve grappled with over the past couple of years, you might get consensus on risks such as the economy, regulation, commodity pricing and consumer demand. Many businesses might also cite brand and reputational risk. But you would probably get little agreement on how they’ve factored such risks into their financial forecasts and planning. Why? One reason may be that some companies haven’t fully done so.

Part of the problem is that financial planning and analysis (FP&A) has not changed fundamentally from the way it was done 10 years ago, despite the onslaught of new and more strategic risks. Many finance chiefs, controllers and FP&A teams still spend months working toward group-level numbers that they agree to communicate to the market. In fact, according to new global research from Deloitte LLP, budgeting takes eight weeks or more in 80% of organizations.¹ Moreover, there still appears to be very little process integration across risk management, strategic planning, financial forecasting and budgeting—integration often considered vital to addressing the speed and range of risks many companies face.

There is clearly a need to reliably reflect volatility more explicitly in the process. Many boards, investors, regulators and rating agencies are demanding greater accuracy in forecasting. Plus, technological advances mean many finance chiefs have access to the tools they need to do proper risk adjustment of their plans, and they can add one more to their toolkit: an analytical framework—risk-adjusted forecasting—that seeks to tame the uncertainties in the FP&A process.

Identifying and Incorporating Risks

Risks are everywhere for finance chiefs—and they’re multiplying. In the 4Q 2013 CFO Signals™ report, the chief worries of North America’s CFOs responding to the survey centered on concerns over long-term growth and the impact of government actions on the economic recovery.²

What’s more, many finance chiefs are well aware that the risks they face pose high risks for their companies. In a survey Deloitte conducted recently with Forbes Insights, strategic risks—those that affect or are created by business strategy decisions such as the pursuit of increased market share—have become a major focus, with 81% of surveyed companies now explicitly managing strategic risk rather than limiting their focus to traditional areas, such as operational, financial and compliance risk.³

Yet, despite this heightened awareness, current FP&A processes are often still woefully inadequate. Granted, many companies typically incorporate “safety buffers” into their forecasts. But safety buffers tend not to have been linked explicitly to the drivers of risk and volatility. Sensitivity analysis typically tackles risk on a variable-by-variable basis rather than simultaneously. Even Monte Carlo analysis simulation is often an experiment rather than actually embedded into the processes. In fact, some common problems in today’s current FP&A processes include:

1. Static view. Traditional forecasts and plans typically use single-point estimates and metrics with little or no discussion of risks and possible variances, and without showing correlations among multiple risks.

2. Guesses rather than facts. Forecasts are often developed by aggregating best guesses from across an enterprise without focusing on risks that could have a major impact on performance, such as competitor actions, talent shortages, cost volatility and regulatory pressures.

3. Inadequate stress testing. Many companies don’t normally stress test their forecasts; when they do, the efforts tend to be limited and focused on a single generic parameter such as price, demand, or input costs.

Given that risks happen in aggregation and often interact, it is a serious oversimplification to look at the drivers of uncertainty in isolation. As the head of strategy at a FTSE 100 company expressed it, “What is required is the ability to make connections, see linkages and patterns that can clump together dangerously.⁴”

Moreover, without a cross-functional view of risk, it can be very difficult to address the burning risk questions that currently face finance and the organization overall: How can the organization grow its brand and improve revenue growth, operating margins and asset productivity in the face of increasing volatility? How can the organization reliably analyze exposure to emerging risks and develop cost-effective mitigation strategies? And, ultimately, how can finance chiefs have greater confidence in the delivery of the budgets and plans to which they are committing?

Enter Risk-adjusted Forecasting

Far from a theoretical solution, risk-adjusted forecasting can offer the answers—and the comfort level—many finance chiefs seek. Using established analytical modeling techniques, the process generates a range of possible outcomes and probabilities based on multiple risk variables, rather than a single variable. Cash-flow and earnings-at-risk measures are calculated by analyzing how financial forecasts could be impacted by major risk drivers and generating a probability distribution (for example, a bell curve) of likely outcomes for each period. Once the model has been fully populated, it can analyze the aggregate impact of multiple risks and also produce a high-level summary of how much each driver may contribute to overall risk levels.

The process works by capturing risks and planning assumptions in a quantitative way that augments traditional estimates and intuition. The number and types of risk drivers may differ for each company depending on the industry and business/operating model, and they are likely to change over time (for example, reservoir uncertainty for an energy company or patent rejection for a pharmaceutical company). But the expanded view can help many companies address interconnected risks, some of which may have been previously identified, others that may have gone unnoticed. The methodology also helps improve strategic planning by providing executives with a better understanding of asset performance, capital allocation and profitability by setting metrics against a backdrop of related risks.

For finance chiefs, the process can offer a powerful decision-making tool. Given that the model can produce earnings or cash-flow distributions in individual years, companies can compare the differences between the budget, the expected value and the realistic worst- and best-case scenarios. That probability of “best” and “worst” cases can then be set at whatever level of confidence is sought—say, 1 in 10 or 1 in 8. And this facilitates a discussion around what an acceptable level of downside uncertainty is, which very often supports the articulation of what the risk appetite of the company and the management group is.

Moreover, since the ranges produced allow finance chiefs to visualize the impact of true volatility, the process can expose errors or inconsistencies in inputs or assumptions and help to promptly correct for them. Say, for example, a company shocks its commodity input prices with (±) 5% in markets that have a monthly volatility of 10%. Visualization of the data can effectively showcase that risk has been underestimated, and adjustments can be made. In other words, the process can provide a much deeper assessment of the uncertainties a company faces in its cash-flow and earnings forecasts, as well as a clearer quantitative understanding of which risks contribute most of the exposure.

Start Small, then Spread the Word

There are multiple hurdles many finance chiefs may have to overcome to fully embrace risk-adjusted modeling at their organizations. For example, despite the potential for bolstering management’s confidence in forecasts, there appears to be an overall lack of awareness about the approach as well as loyalty to the status quo (accentuated by a hefty dose of inertia). In addition, there can be a perceived complexity associated with multivariable stress-testing analysis, which some executives view as intimidating, as well as worries that corporate IT systems may not support the process.

To help overcome such hurdles, consider the following:

Start with a pilot. Companies interested in risk-adjusted forecasting may want to start with a pilot project focused on group-level forecasts or a particular business unit or product P&L. Input to the model (which could just be done on Excel) should be a balanced mix of quantitative data and qualitative insights from subject-matter specialists—information that in many cases already exists within the organization or can be easily obtained. Over time, the pilot can evolve and expand in response to future business requirements. But some benefits of keeping an implementation focused are that it targets specific problems, makes significant quick wins and tangible contributions to competitive positioning, and keeps costs to a minimum.

Make planning top-down and bottom-up. The bottom-up part of planning involves identifying those business drivers that have historically had a greater impact on operations and are actionable, such as increasing or decreasing production levels. The top-down part involves a strategic framing process, especially for identifying the forward-looking factors that could impact operations in the future. Many businesspeople are more comfortable examining available data (the bottom-up step) than having an open-ended planning or brainstorming discussion (the top-down step). For this framing process to work effectively, keep in mind three key principles: invite a broad representation of stakeholders that captures the business’s full value chain, create an environment where participants can speak openly and noncritically about risk and uncertainty, and then ask the hard questions (for instance, How could we be wrong? What would cause outcomes to be much worse than expected?).

Don’t boil the ocean. Risk-adjusted forecasting may allow you to compare a range of possible outcomes. That doesn’t mean, however, that you should include the full range of possibilities in your planning. Trying to capture the effects of more than, say, 10 or 15 risk drivers on your company’s prospects can lead to excessive complexity, calculation time and data points. Instead, consider the benefit of each incremental step of complexity that you’re adding, in terms of data availability, practicality and perceived importance. For many companies, 10 to 15 risk factors is probably a suitable balance between getting valuable insights and not overburdening the organization.

Use existing processes—and technologies. Many companies already have the functionality to deliver risk-adjusted forecasts—they just don’t know it. In fact, much of the ERP functionality required to produce such forecasts already exists, but is likely not being leveraged. A structured discussion between the finance chief, the CIO and the head of FP&A can help identify any gaps that need to be filled.

Visualize the outputs. In many cases, there is a disconnect between how finance chiefs want their forecasts to reflect risk and what those forecasts actually look like. That isn’t the case in all industries, of course. In energy and resources, for example, management is often more familiar with risk analysis and typically delivers forecasts that reflect high volatility and changes in capital expenditure. But one way to close the gap and start the conversation among the stakeholders in the process is to visualize what the new set of outputs and insights might be. Knowing what you want out of FP&A can allow you to bring risk and return together in terms of how you plan, invest and allocate capital within your business.

Categorize your risks. Risk factors obviously vary for different industries as well as for different companies. Knowing what the common risks are, however, can help create a foundation for a pilot program. Take consumer packaged goods, for example. For that industry, we’ve outlined 45 potential risks that can serve as reference points and classified them into three categories: those that immediately lend themselves to inclusion in a fully quantified risk-adjusted framework; a second tranche of risks that are harder to model, but can still be incorporated into such a framework; and a third tranche that might be treated separately, potentially using scenario-planning approaches, due to potential difficulties in securing supporting data. Understanding common risks and how they cascade and interact, provides a basis from which risk-adjusted forecasting frameworks can be developed and then deployed throughout the wider organization.

Be an ambassador. Without the backing of the finance chief, a risk-adjusted forecasting project will not get off the ground. It is no different from other finance processes or methodology-reengineering-type projects that require tone—and action—from the top. Once a finance chief becomes convinced of the forecasting process’s application in finance, however, there should be a road map that allows the finance chief to roll it out across businesses, geographies and products. Otherwise, the inertia that troubles many such projects will likely ground this one. But with the right backing, risk-adjusted forecasting actually offers a way to turn a reactive reporting process into a more dynamic contributor to decision making and insight.

The Benefits of Integration

In a recent Deloitte webcast titled “Risk-adjusted Forecasting and Planning: Balancing the Risk-Return Equation,”⁵ participants were asked what they viewed as the most difficult step in risk framing. Almost half of the 2,600 respondents cited identifying the full set of value and risk drivers; 25% said facilitating an honest, constructive conversation with relevant stakeholders; 16.6% thought using framing results to guide modeling and data collection would prove most difficult; and 8.6% worried about assembling a broad, relevant set of stakeholders.

Admittedly, each could be a barrier to implementing risk-adjusted modeling. Yet, armed with an improved understanding of uncertainty, many companies can react faster to unexpected events. They can also develop more-consistent assumptions both within and across business units, which may foster stronger communication internally and externally. And finance chiefs as custodians of the forecasting process can gain confidence in the delivery of the plan.

Furthermore, the practical application of risk-adjusted approaches within the businesses can help integrate strategic planning with risk and finance, driving more value at the business-unit level and preparing the company to be more nimble. Given the level, speed and global impact of risks currently facing many companies, such an integrated approach should be considered not only a necessity, but also a competitive advantage.

—Excerpted from CFO Insights, produced by Charles Alsdorf, a director in Deloitte Financial Advisory Services LLP’s Valuation Services practice; Hans-Kristian Bryn, a partner with Deloitte UK and leader of the Strategic Risk Advisory practice; and Nick Pope, a director with Deloitte UK.

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About Deloitte Insights

Deloitte’s Insights for C-suite executives and board members provide information and resources to help address the challenges of managing risk for both value creation and protection, as well as increasing compliance requirements.